
# ------------All libraries used in this notebook-------------------------------
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import OneHotEncoder,LabelBinarizer
from sklearn.preprocessing import MinMaxScaler
from scipy.stats import mode
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold, cross_val_score
from sklearn.model_selection import KFold, StratifiedKFold, LeaveOneOut
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.decomposition import PCA
from factor_analyzer import FactorAnalyzer
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFE
import seaborn as sns
from sklearn.metrics import recall_score, f1_score, precision_score, confusion_matrix
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectKBest, chi2, VarianceThreshold, mutual_info_classif
import plotly.express as px
import plotly.graph_objects as go
from sklearn.model_selection import cross_val_predict
from sklearn.feature_selection import SelectFromModel
from sklearn.compose import ColumnTransformer
from scipy.stats import f_oneway

import scipy.stats as stats
from sklearn.metrics import classification_report, accuracy_score, roc_auc_score, roc_curve,auc
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report


#-------------------------- Reading Data from my Local repository----------------------------
""" -------------------Datatset Details------------------------------------
Breast Cancer Wisconsin Data Set used for binary classification tasks to predict whether a breast cancer tumor is malignant (M) or benign (B). Here's a brief overview of the dataset:
Features: The dataset contains 30 numeric features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. These features describe various characteristics of the cell nuclei present in the image.
Target Variable: The target variable is the diagnosis, which is binary: 'M' for malignant and 'B' for benign.

"""
def Data_Load():
    df = pd.read_csv('../Datasets/Classification.CancerMB.csv')
    return df


""" There are 32 Unamed values in dataset that might be irrelevant or empty values so we have to drop it from our dataset frame.
target variable is Diagnosis has binary categorical values B or M and total are 569
I also dropped id columns from dataframe. No missing values"""
def Data_Cleaning(df):
    df.drop(columns=["id", "Unnamed: 32"], axis=1, inplace=True)
    return df


df = Data_Cleaning(Data_Load())
target = 'diagnosis'
df


# # comprehensive EDA report with visualizations and statistical summaries,understand the distribution, statistics, and relationships in  dataset. Help in identifying patterns, outliers, and potential issues in  data
# from dataprep.eda import create_report,plot_missing,plot_correlation, plot

# # Assuming df is your DataFrame
# create_report(df)


def EDA(df):
    target = 'diagnosis'
    # printing five number summary
    summary_stats = df.describe(include='all')
    # creating histogram of categorical columns
    fig = px.histogram(df, x=target, color=target,
                   labels={'count': 'Count', target: target},
                   title="Distribution of " + target)
    
    fig.update_layout(title="Histograms of Target Column", height=400, width=600)
    fig.show()
    # box plots 
    df.boxplot(figsize=(20,10))
    # here it will show pairplot of mean values and target columns
    #PAIR PLOTS of mean values
    columns_mean=('diagnosis', 'radius_mean','texture_mean', 'perimeter_mean','area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean')
    df_mean = pd.DataFrame(df,columns = columns_mean)
    sns.pairplot(df_mean, hue = "diagnosis", diag_kind='kde',palette = ["red","green"])
    # returning summary
    return summary_stats


EDA(df)


# as we knows that there are total 569 instances in our dataframe and from histogram we can calculate malignnat and benign percentages so it will be near to Malignnat 37% and Benign 63% so it mean data is imbalance.
# Box Plots: The box plots depict the distribution of data across different groups or categories, showing the median, quartiles, and potential outliers. Scale differences between the box plots indicate varying magnitudes or ranges of data values. Outliers are data points that fall outside the whiskers of the box plot and are significantly different from the rest of the data


# Splitting data for ease in boolean,numerical and cateogorical columns
def Split_Columns(data):
    num_col=[]
    bool_col=[]
    cat_col=[]    
    for col in data.columns:
        if ((data[col].dtype=='float64') or (data[col].dtype=='int64')):
            num_col.append(col)
        elif (data[col].dtype=='bool'):
            bool_col.append(col)
        else:
            cat_col.append(col)    
    return num_col, bool_col, cat_col
num_col, bool_col, cat_col = Split_Columns(df)


# Correlation analysis
def Correlation_Analysis(df):
    df = df[num_col].corr()
    plt.figure(figsize = (15, 15))
    sns.heatmap(df[num_col].corr(), annot = True)
    return df


Correlation_Analysis(df)


# ------------Result of EDA
# Texture_mean and texture_worst are high correlared with value 0.96
# fraction_dimension_worst and fraction_dimension_mean are also correleted with 0.82 value.
# Radius, permiter and area are correlated as well
# some are normal correlated like texture features etc.  all those who belog to -1 or less are strong negative correlation
# Features related to symmetry are weak positive correlation as we can see in heatmap.


def detect_outliers(df):
    colors = px.colors.qualitative.Pastel  # Get a list of pastel colors for each box plot
    
    for i, column in enumerate(num_col):
        fig = go.Figure()
        fig.add_trace(go.Box(
            y=df[column],
            name=column,
            boxpoints='outliers',
            marker_color=colors[i % len(colors)]  # Use a different color for each box plot
        ))
        
        fig.update_layout(
            title='Boxplot for Outlier Detection',
            xaxis_title='Columns',
            yaxis_title='Values',
            showlegend=False,
            height=400,
            width=600,
            margin=dict(l=50, r=50, t=50, b=50),
            plot_bgcolor='white'
        )
        
        fig.show()


detect_outliers(df)


#calculating skewness values
def calculate_skewness(df):
    num_col = df.select_dtypes(include=['float64', 'int64']).columns
    
    skewness_dfs = []

    for column in num_col:
        skewness = df[column].skew()

        if skewness > 0:
            skewness_type = 'right-skewed'
        elif skewness < 0:
            skewness_type = 'left-skewed'
        else:
            skewness_type = 'approximately symmetric'

        skewness_df = pd.DataFrame({'Column': [column], 'Distribution': [skewness_type], 'Skewness Value': [skewness]})
        skewness_dfs.append(skewness_df)

    skewness_df = pd.concat(skewness_dfs, ignore_index=True)
    return skewness_df


calculate_skewness(df)


# as we can see from box plots f each features and skewness values so it's clear that mostly columns have positivr skewness values. There are not a single column that has negative skewness.


# There are some minor warnings so i wanna remove them
import warnings
warnings.filterwarnings("ignore")
from sklearn.preprocessing import LabelEncoder
Label_Encoder= LabelEncoder()
df['diagnosis'] = Label_Encoder.fit_transform(df['diagnosis'])


# data splitting
def manual_data_splitting(df, target):
    y = df[target]
    X = df.drop(columns=[target])
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
    return X_train, X_test, y_train, y_test


X_train, X_test, y_train, y_test = manual_data_splitting(df,target)


def main_function(df):
    df.drop(columns=[target], inplace = True)
    preprocessor = ColumnTransformer(
        transformers=[
            ('df_scaled', StandardScaler(), df.columns)
        ], remainder='passthrough')
    pipeline = Pipeline([
        ('preprocessor', preprocessor),
        ('classifier', KNeighborsClassifier())
    ])
    return pipeline


myfucntion = main_function(df)


myfucntion.fit(X_train, y_train)

Pipeline(steps=[('preprocessor',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('df_scaled', StandardScaler(),
                                                  Index(['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
       'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst'],
      dtype='object'))])),
                ('classifier', KNeighborsClassifier())])


y_pred = myfucntion.predict(X_test)


report = classification_report(y_test, y_pred)
print('Classification Report:\n', report)
auc = roc_auc_score(y_test, y_pred)
print('AUC:', auc)

Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.99      0.96       105
           1       0.98      0.89      0.94        66

    accuracy                           0.95       171
   macro avg       0.96      0.94      0.95       171
weighted avg       0.95      0.95      0.95       171

AUC: 0.9422077922077923


# calculating accuracy,precision,recall etc
accuracy = accuracy_score(y_test, y_pred)
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')
precision = precision_score(y_test, y_pred, average='weighted')
cm = confusion_matrix(y_test, y_pred)


# show classificaiton report as i calcualted abvoce
mycalculations = {
    'Accuracy': [accuracy],
    'Precision': [precision],
    'Recall': [recall],
    'F1-Score': [f1]
}

# Create DataFrame
metrics = pd.DataFrame(mycalculations)
metrics
# Results shows that model has good performance with high precision,positive predictions


# cross Validations
def cross_validation(myfucntion, X_train, y_train, cv_type):
    # Perform cross-validation
    scores = cross_val_score(myfucntion, X_train, y_train, cv=cv_type)
    return scores.mean()


# 1- KFold cross-validation object
k_fold = KFold(n_splits=5, shuffle=True, random_state=42)
k_fold_accuracy = cross_validation(myfucntion, X_train, y_train, k_fold)
print("K-Fold Cross-Validation Scores:", k_fold_accuracy)
print("Mean K-Fold CV Score:", k_fold_accuracy.mean())

K-Fold Cross-Validation Scores: 0.9672784810126582
Mean K-Fold CV Score: 0.9672784810126582


# Now see predictions on the testing data
y_pred = myfucntion.predict(X_test)
accuracy_k_fold = accuracy_score(y_test, y_pred)
precision_k_fold = precision_score(y_test, y_pred, average='weighted')
cm_k_fold = confusion_matrix(y_test, y_pred)
recall_k_fold = recall_score(y_test, y_pred, average='weighted')
f1_k_fold = f1_score(y_test, y_pred, average='weighted')


# Evaluate metrics with and without cross validation
# Define the data
mycalculation = {
    'Cross-Validation Type': ['Without CV', 'K-Fold'],
    'Cross-Validation Score': ['Without', k_fold_accuracy],
    'Accuracy': [accuracy, accuracy_k_fold],
    'Precision': [precision, precision_k_fold],
    'Recall': [recall, recall_k_fold],
    'F1-Score': [f1, f1_k_fold]
}
metrics = pd.DataFrame(mycalculation)
metrics


# Now let's see the impact by applying another type of cross validation
# 2-Stratified Cross Validation

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
cv_stratified_accuracy = cross_validation(myfucntion, X_train, y_train, skf)
print("Stratified Cross-Validation Scores:", cv_stratified_accuracy)
print("Mean Stratified CV Score:", cv_stratified_accuracy.mean())

Stratified Cross-Validation Scores: 0.9648101265822785
Mean Stratified CV Score: 0.9648101265822785


y_pred = myfucntion.predict(X_test)
accuracy_stratified = accuracy_score(y_test, y_pred)
precision_stratified = precision_score(y_test, y_pred, average='weighted')
recall_stratified = recall_score(y_test, y_pred, average='weighted')
f1_stratified = f1_score(y_test, y_pred, average='weighted')
cm_stratified = confusion_matrix(y_test, y_pred)


# 3- LeaveOneOut cross-validation 
loo = LeaveOneOut()
cv_loo_accuracy = cross_validation(myfucntion, X_train, y_train, loo)
print("Leave-One-Out Cross-Validation Scores:", cv_loo_accuracy)
print("Mean LOOCV Score:", cv_loo_accuracy.mean())

Leave-One-Out Cross-Validation Scores: 0.9698492462311558
Mean LOOCV Score: 0.9698492462311558


y_pred = myfucntion.predict(X_test)
accuracy_loo = accuracy_score(y_test, y_pred)
precision_loo = precision_score(y_test, y_pred, average='weighted')
recall_loo = recall_score(y_test, y_pred, average='weighted')
f1_loo = f1_score(y_test, y_pred, average='weighted')
cm_loo = confusion_matrix(y_test, y_pred)


# Now here i created a table where i show all 3 types results of cross validations
mydata = {
    'Cross-Validation Type': ['Without CV', 'K-Fold', 'Stratified', 'Leave-One-Out'],
    'Cross-Validation Score': ['Without Score', k_fold_accuracy, cv_stratified_accuracy, cv_loo_accuracy],
    'Accuracy': [accuracy, accuracy_k_fold, accuracy_stratified, accuracy_loo],
    'Precision': [precision, precision_k_fold, precision_stratified, precision_loo],
    'Recall': [recall, recall_k_fold, recall_stratified, recall_loo],
    'F1-Score': [f1, f1_k_fold, f1_stratified, f1_loo]
}
metrics = pd.DataFrame(mydata)
metrics

# so here we can see results that CV score are almost equal so we can say that model can perform well on unseen data or we can say that model is npt overfitting on training data. Now we can say confidently that the model is performing well and is likely to generalize well to new data


report = classification_report(y_test, y_pred)
print('Classification Report:\n', report)
auc = roc_auc_score(y_test, y_pred)
print('AUC:', auc)

Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.99      0.96       105
           1       0.98      0.89      0.94        66

    accuracy                           0.95       171
   macro avg       0.96      0.94      0.95       171
weighted avg       0.95      0.95      0.95       171

AUC: 0.9422077922077923


#---------------------------------------------Feature Selection Techniques
def Data():
    df = pd.read_csv('../Datasets/Classification.CancerMB.csv')
    X = df.drop(['diagnosis', 'id', 'Unnamed: 32'], axis=1)
    y = df['diagnosis']
    return X, y


def preprocess(X):
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    X_scaled = X_scaled - X_scaled.min()  # non-negative
    return X_scaled


# Evaluate the model using cross-validation
def evaluate_model(X, y, model):
    scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')
    return scores.mean()
def filter_methods(X, y):
    # Apply Chi-square Test
    chi2_selector = SelectKBest(chi2, k=10)
    X_chi2 = chi2_selector.fit_transform(X, y)
    
    # Apply Variance Threshold
    vt_selector = VarianceThreshold(threshold=0.1)
    X_vt = vt_selector.fit_transform(X)
    
    # Apply Covariance/Mutual Information
    mi_selector = SelectKBest(mutual_info_classif, k=10)
    X_mi = mi_selector.fit_transform(X, y)
    
    return X_chi2, X_vt, X_mi


def wrapper_methods(X, y):
    # Apply SelectKBest with chi2 scoring function
    selector = SelectKBest(score_func=chi2, k=10)
    X_selected = selector.fit_transform(X, y)
    return X_selected


def apply_knn_with_pca(X_train, X_test, y_train, y_test, n_components=5, n_neighbors=5):
    pca = PCA(n_components=n_components)
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)

    knn_with_pca = KNeighborsClassifier(n_neighbors=n_neighbors)
    knn_with_pca.fit(X_train_pca, y_train)

    y_pred_pca = knn_with_pca.predict(X_test_pca)
    accuracy_pca = accuracy_score(y_test, y_pred_pca)

    return accuracy_pca, y_pred_pca

accuracy, y_pred = apply_knn_with_pca(X_train, X_test, y_train, y_test)
print("Classification Accuracy with PCA: {:.2f}%".format(accuracy * 100))

# Classification report
print("Classification Report with PCA:")
print(classification_report(y_test, y_pred))

Classification Accuracy with PCA: 92.40%
Classification Report with PCA:
              precision    recall  f1-score   support

           B       0.93      0.95      0.94       105
           M       0.92      0.88      0.90        66

    accuracy                           0.92       171
   macro avg       0.92      0.92      0.92       171
weighted avg       0.92      0.92      0.92       171


# Preproses and model selection
X,y=Data()
X_scaled = preprocess(X)
knn = KNeighborsClassifier()


variances = df.var()
variances

radius_mean                    12.418920
texture_mean                   18.498909
perimeter_mean                590.440480
area_mean                  123843.554318
smoothness_mean                 0.000198
compactness_mean                0.002789
concavity_mean                  0.006355
concave points_mean             0.001506
symmetry_mean                   0.000752
fractal_dimension_mean          0.000050
radius_se                       0.076902
texture_se                      0.304316
perimeter_se                    4.087896
area_se                      2069.431583
smoothness_se                   0.000009
compactness_se                  0.000321
concavity_se                    0.000911
concave points_se               0.000038
symmetry_se                     0.000068
fractal_dimension_se            0.000007
radius_worst                   23.360224
texture_worst                  37.776483
perimeter_worst              1129.130847
area_worst                 324167.385102
smoothness_worst                0.000521
compactness_worst               0.024755
concavity_worst                 0.043524
concave points_worst            0.004321
symmetry_worst                  0.003828
fractal_dimension_worst         0.000326
dtype: float64


# Filter Methods
X_chi2, X_vt, X_mi = filter_methods(X_scaled, y)

# Wrapper Methods
X_fw = wrapper_methods(X_scaled, y)

# PCA Method
X_pca, explained_variance = pca_method(X_scaled)


# Evaluate KNN on original dataset
print("Original Dataset:", evaluate_model(X_scaled, y, knn))

# Chi-square
print("Chi-square Test:", evaluate_model(X_chi2, y, knn))

# Variance Threshold 
print("Variance Threshold:", evaluate_model(X_vt, y, knn))

#Covariance/Mutual Information 
print("Covariance/Mutual Information:", evaluate_model(X_mi, y, knn))

# Forward/Backward 
print("Forward/Backward Feature Selection:", evaluate_model(X_fw, y, knn))

Original Dataset: 0.9648501785437045
Chi-square Test: 0.9385188635305077
Variance Threshold: 0.9648501785437045
Covariance/Mutual Information: 0.9385343890700202
Forward/Backward Feature Selection: 0.9385188635305077


# PCA Analysis
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
cov_matrix = np.cov(X_scaled.T)

eigenvalues, eigenvectors = np.linalg.eig(cov_matrix)

# Sort the eigenvalues in descending order
sorted_indices = np.argsort(eigenvalues)[::-1]
eigenvalues_sorted = eigenvalues[sorted_indices]

# Calculate the explained variance ratio
explained_variance_ratio = eigenvalues_sorted / np.sum(eigenvalues_sorted)

plt.figure(figsize=(12, 6))
plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, alpha=0.5, align='center')
plt.plot(range(1, len(explained_variance_ratio) + 1), np.cumsum(explained_variance_ratio), marker='o', color='r')
plt.xlabel('Principal Components')
plt.ylabel(' Variance Ratio')
plt.title(' Variance Ratio for PCA')
plt.xticks(range(1, len(explained_variance_ratio) + 1))
plt.grid(True)
plt.show()


report = classification_report(y_test, y_pred)
print('Classification Report:\n', report)
auc = roc_auc_score(y_test, y_pred)
print('AUC:', auc)

Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.99      0.96       105
           1       0.98      0.89      0.94        66

    accuracy                           0.95       171
   macro avg       0.96      0.94      0.95       171
weighted avg       0.95      0.95      0.95       171

AUC: 0.9422077922077923


# From PCA graph, we can see clearly that there's some components have significant portion of the variance in this case we can see 80% ore more so we can seet that these componnets has more important features in our dataset.
#


import matplotlib.pyplot as plt
import numpy as np

# Accuracy scores obtained from each feature selection method
methods = ['Original', 'Chi-square', 'Variance Threshold', 'Covariance/Mutual Info', 'Forward/Backward FS']
accuracy_scores = [0.9648501785437045, 0.9385188635305077, 0.9648501785437045, 0.9385343890700202, 0.9385188635305077]

# Create a bar plot
plt.figure(figsize=(10, 6))
plt.bar(methods, accuracy_scores, color='skyblue')
plt.ylim(0.9, 0.97) 
plt.ylabel('Accuracy')
plt.title('Effect of Feature Selection Methods on KNN Accuracy')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()


"""
The original dataset achieved the highest accuracy at approximately 96.49%. Applying feature selection methods such as Chi-square Test and Covariance/Mutual Information slightly decreased the accuracy to around 93.85% that indicating that these methods may have removed some relevant features.
Variance Threshold had a negligible effect on accuracy, maintaining it at 96.49%. Forward/Backward Feature Selection also resulted in a similar accuracy of 93.85%.
PCA reducing dimensionality led to a slight decrease in accuracy to about 94.03%, suggesting some loss of information. 
So we can say that the original dataset and Variance Threshold method performed the best, while other methods showed minor decreases in accuracy.
"""

'\nThe original dataset achieved the highest accuracy at approximately 96.49%. Applying feature selection methods such as Chi-square Test and Covariance/Mutual Information slightly decreased the accuracy to around 93.85% that indicating that these methods may have removed some relevant features.\nVariance Threshold had a negligible effect on accuracy, maintaining it at 96.49%. Forward/Backward Feature Selection also resulted in a similar accuracy of 93.85%.\nPCA reducing dimensionality led to a slight decrease in accuracy to about 94.03%, suggesting some loss of information. \nSo we can say that the original dataset and Variance Threshold method performed the best, while other methods showed minor decreases in accuracy.\n'


# -------------------------------------------Combine Interpretation
""" As i have applied different approaches and i find some results:
-> Without Cross-Validation: KNN model achieved an accuracy of approximately 95.32% with almsot similar precision,recall and F1 score values. So it indicates that the model performed well on the dataset but might benefit from validation on unseen data.
-> With Cross-Validation: KNN model's performance improved slightly with cross-validation especially with K Fold and Leave-One-Out achieving higher accuracy scores like 96.73% and 96.98% respectively. So it shows that that cross validation helped generalize the model's performance better across different subsets of the dataset.
-> Feature Selection Techniques:
- Variance threshold has higher accuracy of approximately 96.49% mean that it is more effective in selecting relevant features
- I also apply Chi-square Test, Covariance/Mutual Information, Forward/Backward Feature Selection with accuracy around 93.85% mean they might not have captured the most important features in dataset.
- PCA has accuracy of around 92%, while not as high as the variance threshold but it found some important features as work for dimension reduction as i mentioend earlier compaonents and different variance.
So for this Cross Validation perform well as per my results.

"""

" As i have applied different approaches and i find some results:\n-> Without Cross-Validation: KNN model achieved an accuracy of approximately 95.32% with almsot similar precision,recall and F1 score values. So it indicates that the model performed well on the dataset but might benefit from validation on unseen data.\n-> With Cross-Validation: KNN model's performance improved slightly with cross-validation especially with K Fold and Leave-One-Out achieving higher accuracy scores like 96.73% and 96.98% respectively. So it shows that that cross validation helped generalize the model's performance better across different subsets of the dataset.\n-> Feature Selection Techniques:\n- Variance threshold has higher accuracy of approximately 96.49% mean that it is more effective in selecting relevant features\n- I also apply Chi-square Test, Covariance/Mutual Information, Forward/Backward Feature Selection with accuracy around 93.85% mean they might not have captured the most important features in dataset.\n- PCA has accuracy of around 94.03%, while not as high as the variance threshold but it found some important features as work for dimension reduction as i mentioend earlier compaonents and different variance.\nSo for this Cross Validation perform well as per my results.\n\n"

	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	...	25.380	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890
1	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	...	24.990	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902
2	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	...	23.570	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758
3	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	...	14.910	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300
4	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	...	22.540	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	...	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115
565	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	...	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
566	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	...	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
567	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	...	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
568	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	...	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	569	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
unique	2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
top	B	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
freq	357	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
mean	NaN	14.127292	19.289649	91.969033	654.889104	0.096360	0.104341	0.088799	0.048919	0.181162	...	16.269190	25.677223	107.261213	880.583128	0.132369	0.254265	0.272188	0.114606	0.290076	0.083946
std	NaN	3.524049	4.301036	24.298981	351.914129	0.014064	0.052813	0.079720	0.038803	0.027414	...	4.833242	6.146258	33.602542	569.356993	0.022832	0.157336	0.208624	0.065732	0.061867	0.018061
min	NaN	6.981000	9.710000	43.790000	143.500000	0.052630	0.019380	0.000000	0.000000	0.106000	...	7.930000	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500	0.055040
25%	NaN	11.700000	16.170000	75.170000	420.300000	0.086370	0.064920	0.029560	0.020310	0.161900	...	13.010000	21.080000	84.110000	515.300000	0.116600	0.147200	0.114500	0.064930	0.250400	0.071460
50%	NaN	13.370000	18.840000	86.240000	551.100000	0.095870	0.092630	0.061540	0.033500	0.179200	...	14.970000	25.410000	97.660000	686.500000	0.131300	0.211900	0.226700	0.099930	0.282200	0.080040
75%	NaN	15.780000	21.800000	104.100000	782.700000	0.105300	0.130400	0.130700	0.074000	0.195700	...	18.790000	29.720000	125.400000	1084.000000	0.146000	0.339100	0.382900	0.161400	0.317900	0.092080
max	NaN	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	0.304000	...	36.040000	49.540000	251.200000	4254.000000	0.222600	1.058000	1.252000	0.291000	0.663800	0.207500

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
radius_mean	1.000000	0.323782	0.997855	0.987357	0.170581	0.506124	0.676764	0.822529	0.147741	-0.311631	...	0.969539	0.297008	0.965137	0.941082	0.119616	0.413463	0.526911	0.744214	0.163953	0.007066
texture_mean	0.323782	1.000000	0.329533	0.321086	-0.023389	0.236702	0.302418	0.293464	0.071401	-0.076437	...	0.352573	0.912045	0.358040	0.343546	0.077503	0.277830	0.301025	0.295316	0.105008	0.119205
perimeter_mean	0.997855	0.329533	1.000000	0.986507	0.207278	0.556936	0.716136	0.850977	0.183027	-0.261477	...	0.969476	0.303038	0.970387	0.941550	0.150549	0.455774	0.563879	0.771241	0.189115	0.051019
area_mean	0.987357	0.321086	0.986507	1.000000	0.177028	0.498502	0.685983	0.823269	0.151293	-0.283110	...	0.962746	0.287489	0.959120	0.959213	0.123523	0.390410	0.512606	0.722017	0.143570	0.003738
smoothness_mean	0.170581	-0.023389	0.207278	0.177028	1.000000	0.659123	0.521984	0.553695	0.557775	0.584792	...	0.213120	0.036072	0.238853	0.206718	0.805324	0.472468	0.434926	0.503053	0.394309	0.499316
compactness_mean	0.506124	0.236702	0.556936	0.498502	0.659123	1.000000	0.883121	0.831135	0.602641	0.565369	...	0.535315	0.248133	0.590210	0.509604	0.565541	0.865809	0.816275	0.815573	0.510223	0.687382
concavity_mean	0.676764	0.302418	0.716136	0.685983	0.521984	0.883121	1.000000	0.921391	0.500667	0.336783	...	0.688236	0.299879	0.729565	0.675987	0.448822	0.754968	0.884103	0.861323	0.409464	0.514930
concave points_mean	0.822529	0.293464	0.850977	0.823269	0.553695	0.831135	0.921391	1.000000	0.462497	0.166917	...	0.830318	0.292752	0.855923	0.809630	0.452753	0.667454	0.752399	0.910155	0.375744	0.368661
symmetry_mean	0.147741	0.071401	0.183027	0.151293	0.557775	0.602641	0.500667	0.462497	1.000000	0.479921	...	0.185728	0.090651	0.219169	0.177193	0.426675	0.473200	0.433721	0.430297	0.699826	0.438413
fractal_dimension_mean	-0.311631	-0.076437	-0.261477	-0.283110	0.584792	0.565369	0.336783	0.166917	0.479921	1.000000	...	-0.253691	-0.051269	-0.205151	-0.231854	0.504942	0.458798	0.346234	0.175325	0.334019	0.767297
radius_se	0.679090	0.275869	0.691765	0.732562	0.301467	0.497473	0.631925	0.698050	0.303379	0.000111	...	0.715065	0.194799	0.719684	0.751548	0.141919	0.287103	0.380585	0.531062	0.094543	0.049559
texture_se	-0.097317	0.386358	-0.086761	-0.066280	0.068406	0.046205	0.076218	0.021480	0.128053	0.164174	...	-0.111690	0.409003	-0.102242	-0.083195	-0.073658	-0.092439	-0.068956	-0.119638	-0.128215	-0.045655
perimeter_se	0.674172	0.281673	0.693135	0.726628	0.296092	0.548905	0.660391	0.710650	0.313893	0.039830	...	0.697201	0.200371	0.721031	0.730713	0.130054	0.341919	0.418899	0.554897	0.109930	0.085433
area_se	0.735864	0.259845	0.744983	0.800086	0.246552	0.455653	0.617427	0.690299	0.223970	-0.090170	...	0.757373	0.196497	0.761213	0.811408	0.125389	0.283257	0.385100	0.538166	0.074126	0.017539
smoothness_se	-0.222600	0.006614	-0.202694	-0.166777	0.332375	0.135299	0.098564	0.027653	0.187321	0.401964	...	-0.230691	-0.074743	-0.217304	-0.182195	0.314457	-0.055558	-0.058298	-0.102007	-0.107342	0.101480
compactness_se	0.206000	0.191975	0.250744	0.212583	0.318943	0.738722	0.670279	0.490424	0.421659	0.559837	...	0.204607	0.143003	0.260516	0.199371	0.227394	0.678780	0.639147	0.483208	0.277878	0.590973
concavity_se	0.194204	0.143293	0.228082	0.207660	0.248396	0.570517	0.691270	0.439167	0.342627	0.446630	...	0.186904	0.100241	0.226680	0.188353	0.168481	0.484858	0.662564	0.440472	0.197788	0.439329
concave points_se	0.376169	0.163851	0.407217	0.372320	0.380676	0.642262	0.683260	0.615634	0.393298	0.341198	...	0.358127	0.086741	0.394999	0.342271	0.215351	0.452888	0.549592	0.602450	0.143116	0.310655
symmetry_se	-0.104321	0.009127	-0.081629	-0.072497	0.200774	0.229977	0.178009	0.095351	0.449137	0.345007	...	-0.128121	-0.077473	-0.103753	-0.110343	-0.012662	0.060255	0.037119	-0.030413	0.389402	0.078079
fractal_dimension_se	-0.042641	0.054458	-0.005523	-0.019887	0.283607	0.507318	0.449301	0.257584	0.331786	0.688132	...	-0.037488	-0.003195	-0.001000	-0.022736	0.170568	0.390159	0.379975	0.215204	0.111094	0.591328
radius_worst	0.969539	0.352573	0.969476	0.962746	0.213120	0.535315	0.688236	0.830318	0.185728	-0.253691	...	1.000000	0.359921	0.993708	0.984015	0.216574	0.475820	0.573975	0.787424	0.243529	0.093492
texture_worst	0.297008	0.912045	0.303038	0.287489	0.036072	0.248133	0.299879	0.292752	0.090651	-0.051269	...	0.359921	1.000000	0.365098	0.345842	0.225429	0.360832	0.368366	0.359755	0.233027	0.219122
perimeter_worst	0.965137	0.358040	0.970387	0.959120	0.238853	0.590210	0.729565	0.855923	0.219169	-0.205151	...	0.993708	0.365098	1.000000	0.977578	0.236775	0.529408	0.618344	0.816322	0.269493	0.138957
area_worst	0.941082	0.343546	0.941550	0.959213	0.206718	0.509604	0.675987	0.809630	0.177193	-0.231854	...	0.984015	0.345842	0.977578	1.000000	0.209145	0.438296	0.543331	0.747419	0.209146	0.079647
smoothness_worst	0.119616	0.077503	0.150549	0.123523	0.805324	0.565541	0.448822	0.452753	0.426675	0.504942	...	0.216574	0.225429	0.236775	0.209145	1.000000	0.568187	0.518523	0.547691	0.493838	0.617624
compactness_worst	0.413463	0.277830	0.455774	0.390410	0.472468	0.865809	0.754968	0.667454	0.473200	0.458798	...	0.475820	0.360832	0.529408	0.438296	0.568187	1.000000	0.892261	0.801080	0.614441	0.810455
concavity_worst	0.526911	0.301025	0.563879	0.512606	0.434926	0.816275	0.884103	0.752399	0.433721	0.346234	...	0.573975	0.368366	0.618344	0.543331	0.518523	0.892261	1.000000	0.855434	0.532520	0.686511
concave points_worst	0.744214	0.295316	0.771241	0.722017	0.503053	0.815573	0.861323	0.910155	0.430297	0.175325	...	0.787424	0.359755	0.816322	0.747419	0.547691	0.801080	0.855434	1.000000	0.502528	0.511114
symmetry_worst	0.163953	0.105008	0.189115	0.143570	0.394309	0.510223	0.409464	0.375744	0.699826	0.334019	...	0.243529	0.233027	0.269493	0.209146	0.493838	0.614441	0.532520	0.502528	1.000000	0.537848
fractal_dimension_worst	0.007066	0.119205	0.051019	0.003738	0.499316	0.687382	0.514930	0.368661	0.438413	0.767297	...	0.093492	0.219122	0.138957	0.079647	0.617624	0.810455	0.686511	0.511114	0.537848	1.000000

	Cross-Validation Type	Cross-Validation Score	Accuracy	Precision	Recall	F1-Score
0	Without CV	Without Score	0.953216	0.954844	0.953216	0.952752
1	K-Fold	0.967278	0.953216	0.954844	0.953216	0.952752
2	Stratified	0.96481	0.953216	0.954844	0.953216	0.952752
3	Leave-One-Out	0.969849	0.953216	0.954844	0.953216	0.952752

	Column	Distribution	Skewness Value
0	radius_mean	right-skewed	0.942380
1	texture_mean	right-skewed	0.650450
2	perimeter_mean	right-skewed	0.990650
3	area_mean	right-skewed	1.645732
4	smoothness_mean	right-skewed	0.456324
5	compactness_mean	right-skewed	1.190123
6	concavity_mean	right-skewed	1.401180
7	concave points_mean	right-skewed	1.171180
8	symmetry_mean	right-skewed	0.725609
9	fractal_dimension_mean	right-skewed	1.304489
10	radius_se	right-skewed	3.088612
11	texture_se	right-skewed	1.646444
12	perimeter_se	right-skewed	3.443615
13	area_se	right-skewed	5.447186
14	smoothness_se	right-skewed	2.314450
15	compactness_se	right-skewed	1.902221
16	concavity_se	right-skewed	5.110463
17	concave points_se	right-skewed	1.444678
18	symmetry_se	right-skewed	2.195133
19	fractal_dimension_se	right-skewed	3.923969
20	radius_worst	right-skewed	1.103115
21	texture_worst	right-skewed	0.498321
22	perimeter_worst	right-skewed	1.128164
23	area_worst	right-skewed	1.859373
24	smoothness_worst	right-skewed	0.415426
25	compactness_worst	right-skewed	1.473555
26	concavity_worst	right-skewed	1.150237
27	concave points_worst	right-skewed	0.492616
28	symmetry_worst	right-skewed	1.433928
29	fractal_dimension_worst	right-skewed	1.662579

	Cross-Validation Type	Cross-Validation Score	Accuracy	Precision	Recall	F1-Score
0	Without CV	Without	0.953216	0.954844	0.953216	0.952752
1	K-Fold	0.967278	0.953216	0.954844	0.953216	0.952752